1. Abreu, P.H., Santos, M.S., Abreu, M.H., Andrade, B., Silva, D.C.: Predicting breast cancer recurrence using machine learning techniques: a systematic review. ACM Comput. Surv. (CSUR) 49(3), 52 (2016)
2. Amorim, J.P., Domingues, I., Abreu, P.H., Santos, J.: Interpreting deep learning models for ordinal problems. In: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine learning (ESANN), pp. 373–378 (2018)
3. Azur, M.J., Stuart, E.A., Frangakis, C., Leaf, P.J.: Multiple imputation by chained equations: what is it and how does it work? Int. J. Methods Psychiatr. Res. 20, 40–49 (2011)
4. Beaulieu-Jones, B.K., Moore, J.H.: Missing data imputation in the electronic health record using deeply learned autoencoders. In: Altman, R.B., Dunker, A.K., Hunter, L., Ritchie, M.D., Klein, T.E. (eds.) PSB, pp. 207–218 (2017)
5. Charte, D., Charte, F., García, S., del Jesus, M.J., Herrera, F.: A Practical Tutorial on Autoencoders for Nonlinear Feature Fusion: Taxonomy, Models, Software and Guidelines, vol. 44, pp. 78–96. Elsevier (2018)